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In this book, two of America's leading economists provide the first integrated treatment of the conceptual, practical, and empirical foundations for credit risk pricing and risk measurement. Masterfully applying theory to practice, Darrell Duffie and Kenneth Singleton model credit risk for the purpose of measuring portfolio risk and pricing defaultable bonds, credit derivatives, and other securities exposed to credit risk. The methodological rigor, scope, and sophistication of their state-of-the-art account is unparalleled, and its singularly in-depth treatment of pricing and credit derivatives further illuminates a problem that has drawn much attention in an era when financial institutions the world over are revising their credit management strategies. Duffie and Singleton offer critical assessments of alternative approaches to credit-risk modeling, while highlighting the strengths and weaknesses of current practice. Their approach blends in-depth discussions of the conceptual foundations of modeling with extensive analyses of the empirical properties of such credit-related time series as default probabilities, recoveries, ratings transitions, and yield spreads. Both the "structura" and "reduced-form" approaches to pricing defaultable securities are presented, and their comparative fits to historical data are assessed. The authors also provide a comprehensive treatment of the pricing of credit derivatives, including credit swaps, collateralized debt obligations, credit guarantees, lines of credit, and spread options. Not least, they describe certain enhancements to current pricing and management practices that, they argue, will better position financial institutions for future changes in the financial markets. Credit Risk is an indispensable resource for risk managers, traders or regulators dealing with financial products with a significant credit risk component, as well as for academic researchers and students.
International Business in Times of Crisis classifies studies of crises relevant to international business research following a global pandemic which exposed systems failures and fragilities closely across global economic, financial, political, and social systems.
The purpose of this paper is to empirically determine the effects of political instability on economic growth. Using the system-GMM estimator for linear dynamic panel data models on a sample covering up to 169 countries, and 5-year periods from 1960 to 2004, we find that higher degrees of political instability are associated with lower growth rates of GDP per capita. Regarding the channels of transmission, we find that political instability adversely affects growth by lowering the rates of productivity growth and, to a smaller degree, physical and human capital accumulation. Finally, economic freedom and ethnic homogeneity are beneficial to growth, while democracy may have a small negative effect.
We study a longitudinal sample of over one million French workers and over 500,000 employing firms. Real total annual compensation per worker is decomposed into components related to observable characteristics, worker heterogeneity, firm heterogeneity and residual variation. Except for the residual, all components may be correlated in an arbitrary fashion. At the level of the individual, we find that person-effects, especially those not related to observables like education, are the most important source of wage variation in France. Firm-effects, while important, are not as important as person-effects. At the level of firms, we find that enterprises that hire high-wage workers are more productive but not more profitable. They are also more capital and high-skilled employee intensive. Enterprises that pay higher wages, controlling for person-effects, are more productive and more profitable. They are also more capital intensive but are not more high-skilled labor intensive. We also find that person-effects explain 92% of inter-industry wage differentials.
The Census Bureau has recently begun releasing official statistics that measure the movements of firms in and out of business and workers in and out of jobs. The economic analyses in Producer Dynamics exploit this newly available data on establishments, firms, and workers, to address issues in industrial organization, labor, growth, macroeconomics, and international trade. This innovative volume brings together a group of renowned economists to probe topics such as firm dynamics across countries; patterns of employment dynamics; firm dynamics in nonmanufacturing industries such as retail, health services, and agriculture; employer-employee turnover from matched worker/firm data sets; and turnover in international markets. Producer Dynamics will serve as an invaluable reference to economists and policy makers seeking to understand the links between firms and workers, and the sources of economic dynamics, in the age of globalization.
Until about twenty years ago, the consensus view on the cause of financial-system distress was fairly simple: a run on one bank could easily turn to a panic involving runs on all banks, destroying some and disrupting the financial system. Since then, however, a series of events—such as emerging-market debt crises, bond-market meltdowns, and the Long-Term Capital Management episode—has forced a rethinking of the risks facing financial institutions and the tools available to measure and manage these risks. The Risks of Financial Institutions examines the various risks affecting financial institutions and explores a variety of methods to help institutions and regulators more accurately measure and forecast risk. The contributors--from academic institutions, regulatory organizations, and banking--bring a wide range of perspectives and experience to the issue. The result is a volume that points a way forward to greater financial stability and better risk management of financial institutions.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises. One of the first books to address the challenges of measuring statistical risk from a system-wide persepective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.
In The Economics of Consumption, Tullio Jappelli and Luigi Pistaferri provide a comprehensive examination of the most important developments in the field of consumption decisions and evaluate economic models against empirical evidence.